Design Optimization of the Impeller and Volute of a Centrifugal Pump to Improve the Hydraulic Performance and Flow Stability

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Hyeon-Seok Shim ◽  
Kwang-Yong Kim
Keyword(s):  
Design Optimization ◽  
Centrifugal Pump ◽  
Flow Instability ◽  
Hydraulic Performance ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Recovery Coefficient ◽  
Cross Sectional ◽  
Baseline Design ◽  
Multi Objective

Abstract Multi-objective design optimization was applied to the impeller and volute of a centrifugal pump using surrogate-based optimization techniques and three-dimensional Reynolds-averaged Navier–Stokes (RANS) analysis. The objective functions used to improve the hydraulic performance and operating stability of the pump were the hydraulic efficiency at the design condition and the flow rate at which the maximum volute pressure recovery coefficient occurs. Three design variables were selected based on the results of a sensitivity analysis: the blade outlet angle, the constants in determining the impeller outlet width, and the cross-sectional area of the volute. Using response surface approximation (RSA), surrogate models were constructed for the objective functions based on numerical results at experimental points obtained by Latin hypercube sampling (LHS). The representative Pareto-optimal solutions obtained by the multi-objective genetic algorithm (MOGA) show enhanced objective function values compared to the baseline design. The results of unsteady calculation show that the flow instability of the centrifugal pump was successfully suppressed by the optimization.

Design optimization of hopper cars employing functionally graded honeycomb sandwich panels

2021 ◽  
pp. 095440972110496
Author(s):  
Ayman Al-Sukhon ◽  
Mostafa SA ElSayed
Keyword(s):  
Design Optimization ◽  
Sandwich Panels ◽  
Design Procedure ◽  
Functionally Graded ◽  
Sandwich Composite ◽  
Wall Structure ◽  
Concentrate Mass ◽  
Cross Sectional ◽  
Baseline Design ◽  
Honeycomb Sandwich

In this paper, a novel multiscale and multi-stage structural design optimization procedure is developed for the weight minimization of hopper cars. The procedure is tested under various loading conditions according to guidelines established by regulatory bodies, as well as a novel load case that considers fluid-structure interaction by means of explicit finite elements employing Smoothed Particle Hydrodynamics. The first stage in the design procedure involves topology optimization whereby optimal beam locations are determined within the design space of the hopper car wall structure. This is followed by cross-sectional sizing of the frame to concentrate mass in critical regions of the hopper car. In the second stage, hexagonal honeycomb sandwich panels are considered in lower load regions, and are optimized by means of Multiscale Design Optimization (MSDO). The MSDO drew upon the Kreisselmeier–Steinhausser equations to calculate a penalized cost function for the mass and compliance of a hopper car Finite Element Model (FEM) at the mesoscale. For each iteration in the MSDO, the FEM was updated with homogenized sandwich composite properties according to four design variables of interest at the microscale. A cost penalty is summed with the base cost by comparing results of the FEM with the imposed constraints. Efficacy of the novel design methodology is compared according to a baseline design employing conventional materials. By invoking the proposed methodology in a case study, it is demonstrated that a mass savings as high as 16.36% can be yielded for a single hopper car, which translates into a reduction in greenhouse gas emissions of 13.09% per car based on available literature.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

A Strategy for Multi-Point Compressor Blading Using Multi-Objective Optimization

2012 ◽  
Author(s):  
Alireza Fathi ◽  
Abdollah Shadaram ◽  
Mohammad Alizadeh
Keyword(s):  
Incidence Angle ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Design System ◽  
Design Calculation ◽  
Blade Design ◽  
Objective Functions ◽  
Multi Objective ◽  
First Case ◽  
Through Flow

This paper introduces a framework to perform a multi-objective multipoint aerodynamic optimization for an axial compressor blade. This framework considers through-flow design requirements and mechanical and manufacturing constraints. Typically, components of a blade design system include geometry generation tools, optimization algorithms, flow solvers, and objective functions. In particular, optimization algorithms and objective functions are tuned to reduce blade design calculation cost and to match designed blade performance to the through flow design criteria and mechanical and manufacturing constrains. In the present study, geometry parameters of blade are classified to three categories. For each category, a distinct optimization loop is applied. In outer loop, Gradient-based optimization techniques are used to optimize parameters of the second category and a two-dimensional compressible viscous flow code is used to simulate the cascade fluid flow. Surface curvature optimization is carried out in inner loop, and its objective function is defined by integrating the normalized curvature and curvature slope. The genetic algorithm is used to optimize the parameters in the interior loop. To highlight the capabilities of the design method and to develop design know-how, an initial profile is optimized with three different design philosophies. The highest performance improvement in the first case is 15% reduction in loss at design incidence angle. In the second case, 16.5% increase in allowable incidence angle range, improves blade’s performance at off design conditions.

Analysis and Optimization of Staggered Partial Diffuser Vanes in a Centrifugal Pump

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Hyeon-Seok Shim ◽  
Sang-Hoon Kim ◽  
Kwang-Yong Kim
Keyword(s):  
Centrifugal Pump ◽  
Stokes Equations ◽  
Maximum Pressure ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Baseline Design ◽  
Operating Range ◽  
Full Height

Abstract A performance analysis and three-objective design optimization were performed for the staggered partial diffuser vanes in a centrifugal pump using three-dimensional Reynolds-averaged Navier–Stokes equations. First, the performance of the diffuser vanes was evaluated for four different arrangements: full-height diffuser vanes, vaneless diffuser, half vanes attached to the hub, half vanes attached to the shroud, and staggered vanes attached alternately to the hub and the shroud. The staggered partial diffuser vanes were optimized using the following design variables: the installation angle of the vanes, the heights of the vanes attached to the hub and shroud, and the angle of rotation of the straight part on the pressure surface of the vanes. The objective functions were the hydraulic efficiency, the flowrate of the maximum pressure recovery, and the operating range of the diffuser. The Kriging model was used to construct surrogate models of the objective functions based on the results at the design points obtained by Latin hypercube sampling. The Pareto-optimal solutions were obtained by a multi-objective genetic algorithm (MOGA). The representative Pareto-optimal solutions for the staggered diffuser vanes obtained by the K-means clustering showed the improved performances in terms of both the hydraulic performance and operating range compared with the full-height diffuser vanes and the baseline design.

Relationship Between Flow Instability and Performance of a Centrifugal Pump With a Volute

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Hyeon-Seok Shim ◽  
Kwang-Yong Kim
Keyword(s):  
Centrifugal Pump ◽  
Numerical Solutions ◽  
Flow Instability ◽  
Pressure Fluctuations ◽  
Three Dimensional ◽  
Performance Characteristics ◽  
Recovery Coefficient ◽  
Partial Load ◽  
And Performance ◽  
Static Head

Abstract Flow instability and its correlations with performance characteristics were investigated for a centrifugal pump with a volute. Unsteady three-dimensional Reynolds-averaged Navier–Stokes analysis was performed to analyze the flow and performance characteristics using the shear stress transport (SST) turbulence model. The grid dependence and temporal resolution were tested to evaluate the numerical uncertainties, and the numerical solutions were validated using experimental data. The total-to-static head coefficient, the impeller's total-to-static head coefficient, and the volute static pressure recovery coefficient were selected as performance parameters. To identify the flow instability, pressure fluctuations were monitored upstream of the impeller, at the volute inlet, and on the shroud wall of the impeller. Three different types of flow instability were detected in partial-load conditions: inside the volute, upstream of the impeller, and at the interface between the impeller and volute. The time-dependent flow structures were investigated to obtain insight into the onset of the flow instability. The correlation of the onset of the flow instability with the performance curves was discussed.

scholarly journals Comparison of Planar Parallel Manipulator Architectures Based on a Multi-Objective Design Optimization Approach

2010 ◽  
Author(s):  
Damien Chablat ◽  
Ste´phane Caro ◽  
Raza Ur-Rehman ◽  
Philippe Wenger
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Parallel Manipulator ◽  
Optimization Problem ◽  
Optimization Approach ◽  
Objective Functions ◽  
Multi Objective ◽  
Planar Parallel Manipulator ◽  
Moving Platform ◽  
Pareto Frontiers

This paper deals with the comparison of planar parallel manipulator architectures based on a multi-objective design optimization approach. The manipulator architectures are compared with regard to their mass in motion and their regular workspace size, i.e., the objective functions. The optimization problem is subject to constraints on the manipulator dexterity and stiffness. For a given external wrench, the displacements of the moving platform have to be smaller than given values throughout the obtained maximum regular dexterous workspace. The contributions of the paper are highlighted with the study of 3-PRR, 3-RPR and 3-RRR planar parallel manipulator architectures, which are compared by means of their Pareto frontiers obtained with a genetic algorithm.

On the Entropy of Multi-Objective Design Optimization Solution Sets

2002 ◽  
Author(s):  
Ali Farhang-Mehr ◽  
Shapour Azarm
Keyword(s):  
Design Optimization ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Solution Sets ◽  
Multi Objective ◽  
Speed Reducer ◽  
Quantitative Basis ◽  
Multiobjective Design

In this paper, an entropy-based metric is presented for quality assessment of non-dominated solution sets obtained from a multiobjective optimization technique. This metric quantifies the ‘goodness’ of a solution set in terms of its distribution quality over the Pareto-optimal frontier. Therefore, it can be useful in comparison studies of different multi-objective optimization techniques, such as Multi-Objective Genetic Algorithms (MOGAs), wherein the capabilities of such techniques to produce and maintain diversity among different solution points are desired to be compared on a quantitative basis. An engineering test example, the multiobjective design optimization of a speed-reducer, is presented in order to demonstrate an application of the proposed entropy metric.

Geometric Size Optimization of Annular Step Fin Using Multi-Objective Genetic Algorithm

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Abhijit Deka ◽  
Dilip Datta
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Heat Transfer Rate ◽  
Transfer Rate ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Cross Sectional ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

Although an annular stepped fin can produce better cooling effect in comparison to an annular disk fin, it is yet to be studied in detail. In the present work, one-dimensional heat transfer in a two-stepped rectangular cross-sectional annular fin with constant base temperature and variable thermal conductivity is modeled as a multi-objective optimization problem. Taking cross-sectional half-thicknesses and outer radii of the two fin steps as design variables, an attempt is made to obtain the efficient fin geometry primarily by simultaneously maximizing the heat transfer rate and minimizing the fin volume. For further assessment of the fin performance, three more objective functions are studied, which are minimization of the fin surface area and maximization of the fin efficiency and effectiveness. Evaluating the heat transfer rate through the hybrid spline difference method, the well-known multi-objective genetic algorithm, namely, nondominated sorting genetic algorithm II (NSGA-II), is employed for approximating the Pareto-optimal front containing a set of tradeoff solutions in terms of different combinations of the considered five objective functions. The Pareto-optimal sensitivity is also analyzed for studying the influences of the design variables on the objective functions. As an outcome, it can be concluded that the proposed procedure would give an open choice to designers to lead to a practical stepped fin configuration.

Application of multi-objective optimization techniques to improve the aerodynamic performance of a tunnel ventilation jet fan

2014 ◽  
Vol 229 (1) ◽  
pp. 91-105 ◽  
Author(s):  
Joon-Hyung Kim ◽  
Jin-Hyuk Kim ◽  
Joon-Yong Yoon ◽  
Young-Seok Choi ◽  
Sang-Ho Yang
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Optimization Model ◽  
Forward Direction ◽  
Aerodynamic Performance ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Tunnel Ventilation ◽  
Multi Objective ◽  
Jet Fan

This paper describes the design optimization of a tunnel ventilation jet fan through multi-objective optimization techniques. Four design variables were selected for design optimization. To analyze the performance of the fan, numerical analyses were conducted, and three-dimensional Reynolds-averaged Navier–Stokes equations with a shear stress transport turbulence model were solved. Two objective functions, the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity, were employed, and multi-objective optimization was carried out to improve the aerodynamic performance. A response surface approximation surrogate model was constructed for each objective function based on numerical solutions obtained at specified design points. The non-dominated sorting genetic algorithm with a local search procedure was used for multi-objective optimization. The tradeoff between the two objectives was determined and described with respect to the Pareto-optimal solutions. Based on the analysis of the optimization results, we propose an optimization model to satisfy the objective function. Finally, to verify the performance, experiments with the base model and the optimization model were carried out.

Multi-Objective Optimization of Nonlinear Controller for Untripped Rollover Prevention of an 8-dof Vehicle Dynamic Model

2015 ◽  
Vol 775 ◽  
pp. 347-351
Author(s):  
Javad Rezapour ◽  
Behzad Bahrami Joo ◽  
Ali Jamali ◽  
Nader Nariman-Zadeh
Keyword(s):  
Dynamic Model ◽  
Sliding Mode ◽  
Optimization Techniques ◽  
Vehicle Dynamic ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nonlinear Controller ◽  
Multi Objective ◽  
Rollover Prevention ◽  
Vehicle Dynamic Model

The pareto optimal controller design on a two-track vehicle dynamic model with 8-degrees of freedom considering two simultaneous conflicting objective functions has been made in order to prevent the rollover phenomena. In this regard, the requisiteness of trading off among the conflicting requirements of vehicle dynamic control results in utilizing multi-objective optimization techniques. More effectively, a multi-objective uniform-diversity genetic algorithm (MUGA) with a diversity preserving mechanism known as the ε-elimination algorithm has been used to optimize multi input multi output (MIMO) sliding mode controller. The most important conflicting objective functions that have been considered for minimization in this study are, control effort and vehicle roll angle, correspondingly. Finally, The comparison of the obtained results with those in the literature demonstrates that the strategy employed cause remarkable improvement in rollover prevention and maneuverability.

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